// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H

namespace Eigen {

/** \class TensorPatch
 * \ingroup CXX11_Tensor_Module
 *
 * \brief Tensor patch class.
 *
 *
 */
namespace internal {
template<typename PatchDim, typename XprType>
struct traits<TensorPatchOp<PatchDim, XprType>> : public traits<XprType>
{
	typedef typename XprType::Scalar Scalar;
	typedef traits<XprType> XprTraits;
	typedef typename XprTraits::StorageKind StorageKind;
	typedef typename XprTraits::Index Index;
	typedef typename XprType::Nested Nested;
	typedef typename remove_reference<Nested>::type _Nested;
	static const int NumDimensions = XprTraits::NumDimensions + 1;
	static const int Layout = XprTraits::Layout;
	typedef typename XprTraits::PointerType PointerType;
};

template<typename PatchDim, typename XprType>
struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense>
{
	typedef const TensorPatchOp<PatchDim, XprType>& type;
};

template<typename PatchDim, typename XprType>
struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType>>::type>
{
	typedef TensorPatchOp<PatchDim, XprType> type;
};

} // end namespace internal

template<typename PatchDim, typename XprType>
class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors>
{
  public:
	typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
	typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
	typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
	typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
		: m_xpr(expr)
		, m_patch_dims(patch_dims)
	{
	}

	EIGEN_DEVICE_FUNC
	const PatchDim& patch_dims() const { return m_patch_dims; }

	EIGEN_DEVICE_FUNC
	const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

  protected:
	typename XprType::Nested m_xpr;
	const PatchDim m_patch_dims;
};

// Eval as rvalue
template<typename PatchDim, typename ArgType, typename Device>
struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
{
	typedef TensorPatchOp<PatchDim, ArgType> XprType;
	typedef typename XprType::Index Index;
	static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1;
	typedef DSizes<Index, NumDims> Dimensions;
	typedef typename XprType::Scalar Scalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
	static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
	typedef StorageMemory<CoeffReturnType, Device> Storage;
	typedef typename Storage::Type EvaluatorPointerType;

	enum
	{
		IsAligned = false,
		PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
		BlockAccess = false,
		PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
		Layout = TensorEvaluator<ArgType, Device>::Layout,
		CoordAccess = false,
		RawAccess = false
	};

	//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
	typedef internal::TensorBlockNotImplemented TensorBlock;
	//===--------------------------------------------------------------------===//

	EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
		: m_impl(op.expression(), device)
	{
		Index num_patches = 1;
		const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
		const PatchDim& patch_dims = op.patch_dims();
		if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
			for (int i = 0; i < NumDims - 1; ++i) {
				m_dimensions[i] = patch_dims[i];
				num_patches *= (input_dims[i] - patch_dims[i] + 1);
			}
			m_dimensions[NumDims - 1] = num_patches;

			m_inputStrides[0] = 1;
			m_patchStrides[0] = 1;
			for (int i = 1; i < NumDims - 1; ++i) {
				m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1];
				m_patchStrides[i] = m_patchStrides[i - 1] * (input_dims[i - 1] - patch_dims[i - 1] + 1);
			}
			m_outputStrides[0] = 1;
			for (int i = 1; i < NumDims; ++i) {
				m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
			}
		} else {
			for (int i = 0; i < NumDims - 1; ++i) {
				m_dimensions[i + 1] = patch_dims[i];
				num_patches *= (input_dims[i] - patch_dims[i] + 1);
			}
			m_dimensions[0] = num_patches;

			m_inputStrides[NumDims - 2] = 1;
			m_patchStrides[NumDims - 2] = 1;
			for (int i = NumDims - 3; i >= 0; --i) {
				m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1];
				m_patchStrides[i] = m_patchStrides[i + 1] * (input_dims[i + 1] - patch_dims[i + 1] + 1);
			}
			m_outputStrides[NumDims - 1] = 1;
			for (int i = NumDims - 2; i >= 0; --i) {
				m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
			}
		}
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

	EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/)
	{
		m_impl.evalSubExprsIfNeeded(NULL);
		return true;
	}

	EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
	{
		Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
		// Find the location of the first element of the patch.
		Index patchIndex = index / m_outputStrides[output_stride_index];
		// Find the offset of the element wrt the location of the first element.
		Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index];
		Index inputIndex = 0;
		if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
			EIGEN_UNROLL_LOOP
			for (int i = NumDims - 2; i > 0; --i) {
				const Index patchIdx = patchIndex / m_patchStrides[i];
				patchIndex -= patchIdx * m_patchStrides[i];
				const Index offsetIdx = patchOffset / m_outputStrides[i];
				patchOffset -= offsetIdx * m_outputStrides[i];
				inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
			}
		} else {
			EIGEN_UNROLL_LOOP
			for (int i = 0; i < NumDims - 2; ++i) {
				const Index patchIdx = patchIndex / m_patchStrides[i];
				patchIndex -= patchIdx * m_patchStrides[i];
				const Index offsetIdx = patchOffset / m_outputStrides[i + 1];
				patchOffset -= offsetIdx * m_outputStrides[i + 1];
				inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
			}
		}
		inputIndex += (patchIndex + patchOffset);
		return m_impl.coeff(inputIndex);
	}

	template<int LoadMode>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
	{
		EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
		eigen_assert(index + PacketSize - 1 < dimensions().TotalSize());

		Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
		Index indices[2] = { index, index + PacketSize - 1 };
		Index patchIndices[2] = { indices[0] / m_outputStrides[output_stride_index],
								  indices[1] / m_outputStrides[output_stride_index] };
		Index patchOffsets[2] = { indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
								  indices[1] - patchIndices[1] * m_outputStrides[output_stride_index] };

		Index inputIndices[2] = { 0, 0 };
		if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
			EIGEN_UNROLL_LOOP
			for (int i = NumDims - 2; i > 0; --i) {
				const Index patchIdx[2] = { patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i] };
				patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
				patchIndices[1] -= patchIdx[1] * m_patchStrides[i];

				const Index offsetIdx[2] = { patchOffsets[0] / m_outputStrides[i],
											 patchOffsets[1] / m_outputStrides[i] };
				patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
				patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];

				inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
				inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
			}
		} else {
			EIGEN_UNROLL_LOOP
			for (int i = 0; i < NumDims - 2; ++i) {
				const Index patchIdx[2] = { patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i] };
				patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
				patchIndices[1] -= patchIdx[1] * m_patchStrides[i];

				const Index offsetIdx[2] = { patchOffsets[0] / m_outputStrides[i + 1],
											 patchOffsets[1] / m_outputStrides[i + 1] };
				patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i + 1];
				patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i + 1];

				inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
				inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
			}
		}
		inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
		inputIndices[1] += (patchIndices[1] + patchOffsets[1]);

		if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
			PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
			return rslt;
		} else {
			EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
			values[0] = m_impl.coeff(inputIndices[0]);
			values[PacketSize - 1] = m_impl.coeff(inputIndices[1]);
			EIGEN_UNROLL_LOOP
			for (int i = 1; i < PacketSize - 1; ++i) {
				values[i] = coeff(index + i);
			}
			PacketReturnType rslt = internal::pload<PacketReturnType>(values);
			return rslt;
		}
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
	{
		const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() + TensorOpCost::MulCost<Index>() +
											   2 * TensorOpCost::AddCost<Index>());
		return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
	}

	EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
	// binding placeholder accessors to a command group handler for SYCL
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_impl.bind(cgh); }
#endif

  protected:
	Dimensions m_dimensions;
	array<Index, NumDims> m_outputStrides;
	array<Index, NumDims - 1> m_inputStrides;
	array<Index, NumDims - 1> m_patchStrides;

	TensorEvaluator<ArgType, Device> m_impl;
};

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
